Iterative Human Segmentation from Detection Windows using Contour Segment Analysis

This paper presents a new algorithm for human segmentation in images. The human silhouette is estimated in positive windows that are already obtained with an existing efficient detection method. This accurate segmentation uses the data previously computed in the detection. First, a pre-segmentation step computes the likelihood of contour segments as being a part of a human silhouette. Then, a contour segment oriented graph is constructed from the shape continuity cue and the prior cue obtained by the pre-segmentation. Segmentation is so posed as the computation of the shortest-path cycle which corresponds to the human silhouette. Additionally, the process is achieved iteratively to eliminate irrelevant paths and to increase the segmentation performance. The approach is tested on a human image database and the segmentation performance is evaluated quantitatively. Figure 1: A detection window containing a pedestrian (a), contour image computed with the Canny’s algorithm (b), contour pixels gathered in contour segments (c), cells likelihood provided by SVM (d), likely segments computed by the pre-segmentation (e) and segmented silhouette obtained by our method (f).